Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/food") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nateraw/food") model = AutoModelForImageClassification.from_pretrained("nateraw/food") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23867e6c1d0d658fde8ca967272cc98a45c3fb4dd1e7de4f9a19f357ddc2e7bb
- Size of remote file:
- 344 MB
- SHA256:
- 33d6b7d44c0bb524fbb6f6d3b7d1f7c8d7c703b58882bf2a845355d9411f566d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.